Large Model Era: Deep Learning in Osteoporosis Drug Discovery

Area of Science:

• Biomedical Engineering
• Computational Biology
• Pharmacology

Background:

• Osteoporosis, a bone tissue degradation, leads to fractures and reduced quality of life.
• Drug discovery for osteoporosis faces challenges like long development cycles and high costs.
• Deep learning (DL) offers powerful data processing for drug discovery.

Purpose of the Study:

• To review traditional and large DL models in drug discovery.
• To summarize DL applications in all stages of osteoporosis drug discovery.
• To analyze the prospects and limitations of large models in this field.

Main Methods:

• Systematic review of deep learning applications in drug discovery.
• Analysis of traditional and large DL models.
• Discussion of model advantages and limitations.

Main Results:

• Deep learning models are increasingly applied across drug discovery stages.
• Large models present new opportunities for understanding disease mechanisms and drug development.
• Specific applications and prospects in osteoporosis drug discovery are detailed.

Conclusions:

• Deep learning, especially large models, shows significant promise for efficient osteoporosis drug discovery.
• Understanding the advantages and limitations of large models is crucial for future research.
• This review provides a comprehensive overview to guide future drug discovery efforts.